chore: add heavy logging

New translations from
[Crowdin](https://crowdin.com/project/ente-authenticator-app)

mobile/apps/.gitignore

@@ -1 +0,0 @@
-surprise/

mobile/apps/auth/lib/l10n/arb/app_el.arb

@@ -88,6 +88,8 @@
   "useRecoveryKey": "Χρήση κλειδιού ανάκτησης",
   "incorrectPasswordTitle": "Λάθος κωδικός πρόσβασης",
   "welcomeBack": "Καλωσορίσατε και πάλι!",
+  "emailAlreadyRegistered": "Το email είναι ήδη καταχωρημένο.",
+  "emailNotRegistered": "Το email δεν έχει καταχωρηθεί.",
   "madeWithLoveAtPrefix": "φτιαγμένη με ❤️ στο ",
   "supportDevs": "Εγγραφείτε στο <bold-green>ente</bold-green> για να μας υποστηρίξετε",
   "supportDiscount": "Χρησιμοποιήστε τον κωδικό κουπονιού \"AUTH\" για να λάβετε 10% έκπτωση για τον πρώτο χρόνο",
@@ -171,6 +173,7 @@
   "invalidQRCode": "Μη έγκυρος κωδικός QR",
   "noRecoveryKeyTitle": "Χωρίς κλειδί ανάκτησης;",
   "enterEmailHint": "Εισάγετε τη διεύθυνση email σας",
+  "enterNewEmailHint": "Εισάγετε την διεύθυνση ηλ. ταχυδρομείου σας",
   "invalidEmailTitle": "Μη έγκυρη διεύθυνση email",
   "invalidEmailMessage": "Παρακαλούμε εισάγετε μια έγκυρη διεύθυνση email.",
   "deleteAccount": "Διαγραφή λογαριασμού",
@@ -258,6 +261,10 @@
   "areYouSureYouWantToLogout": "Είστε σίγουροι ότι θέλετε να αποσυνδεθείτε;",
   "yesLogout": "Ναι, αποσύνδεση",
   "exit": "Εξοδος",
+  "theme": "Θέμα",
+  "lightTheme": "Φωτεινό",
+  "darkTheme": "Σκοτεινό",
+  "systemTheme": "Σύστημα",
   "verifyingRecoveryKey": "Επαλήθευση κλειδιού ανάκτησης...",
   "recoveryKeyVerified": "Το κλειδί ανάκτησης επαληθεύτηκε",
   "recoveryKeySuccessBody": "Τέλεια! Το κλειδί ανάκτησης σας είναι έγκυρο. Σας ευχαριστούμε για την επαλήθευση.\n\nΠαρακαλώ θυμηθείτε να κρατήσετε το κλειδί ανάκτησης σας και σε αντίγραφο ασφαλείας.",
@@ -490,5 +497,24 @@
   "appLockNotEnabled": "Το κλείδωμα εφαρμογής δεν είναι ενεργοποιημένο",
   "appLockNotEnabledDescription": "Παρακαλώ ενεργοποιήστε το κλείδωμα εφαρμογής μέσω της επιλογής Ασφάλεια > Κλείδωμα εφαρμογής",
   "authToViewPasskey": "Παρακαλώ πιστοποιηθείτε για να δείτε το κλειδί πρόσβασης",
-  "appLockOfflineModeWarning": "Έχετε επιλέξει να προχωρήσετε χωρίς αντίγραφα ασφαλείας. Αν ξεχάσετε τον κωδικό της εφαρμογής, θα κλειδωθείτε από την πρόσβαση στα δεδομένα σας."
+  "appLockOfflineModeWarning": "Έχετε επιλέξει να προχωρήσετε χωρίς αντίγραφα ασφαλείας. Αν ξεχάσετε τον κωδικό της εφαρμογής, θα κλειδωθείτε από την πρόσβαση στα δεδομένα σας.",
+  "duplicateCodes": "Διπλότυποι κωδικοί",
+  "noDuplicates": "✨ Δεν υπάρχουν διπλότυπα",
+  "youveNoDuplicateCodesThatCanBeCleared": "Δεν υπάρχουν διπλότυπα αρχεία που μπορούν να εκκαθαριστούν",
+  "deduplicateCodes": "Διπλότυποι κωδικοί",
+  "deselectAll": "Αποεπιλογή όλων",
+  "selectAll": "Επιλογή όλων",
+  "deleteDuplicates": "Διαγραφή διπλότυπων",
+  "plainHTML": "Απλό HTML",
+  "dropReviewiOS": "Αφήστε μια κριτική στο App Store",
+  "dropReviewAndroid": "Αφήστε μια κριτική στο Play Store",
+  "giveUsAStarOnGithub": "Δώστε μας ένα αστέρι στο Github",
+  "free5GB": "5GB δωρεάν στο <bold-green>ente</bold-green> Photos",
+  "freeStorageOffer": "10% έκπτωση στο <bold-green>ente</bold-green> photos",
+  "freeStorageOfferDescription": "Χρησιμοποιήστε τον κωδικό \"AUTH\" για να λάβετε 10% έκπτωση για τον πρώτο χρόνο",
+  "advanced": "Για προχωρημένους",
+  "algorithm": "Αλγόριθμος",
+  "type": "Τύπος",
+  "period": "Περίοδος",
+  "digits": "Ψηφία"
 }

mobile/apps/auth/lib/l10n/arb/app_pl.arb

@@ -45,7 +45,7 @@
   "timeBasedKeyType": "Oparte na czasie (TOTP)",
   "counterBasedKeyType": "Oparte na liczniku (HOTP)",
   "saveAction": "Zapisz",
-  "nextTotpTitle": "następny",
+  "nextTotpTitle": "dalej",
   "deleteCodeTitle": "Usunąć kod?",
   "deleteCodeMessage": "Czy na pewno chcesz usunąć ten kod? Ta akcja jest nieodwracalna.",
   "trashCode": "Przenieść kod do kosza?",

mobile/apps/photos/lib/main.dart

@@ -185,10 +185,13 @@ Future<void> _runMinimally(String taskId, TimeLogger tlog) async {
   // only runs for android
   await _homeWidgetSync(true);
 
-  // await MLService.instance.init();
-  // await PersonService.init(entityService, MLDataDB.instance, prefs);
-  // await MLService.instance.runAllML(force: true);
-  await smartAlbumsService.syncSmartAlbums();
+  final isDeviceHealthy = await computeController.isDeviceHealthyFuture();
+  if (isDeviceHealthy) {
+    await MLService.instance.init();
+    await PersonService.init(entityService, MLDataDB.instance, prefs);
+    await MLService.instance.runAllML(force: true);
+    await smartAlbumsService.syncSmartAlbums();
+  }
 }
 
 Future<void> _init(bool isBackground, {String via = ''}) async {

mobile/apps/photos/lib/services/machine_learning/compute_controller.dart

@@ -8,6 +8,7 @@ import "package:flutter/foundation.dart";
 import "package:logging/logging.dart";
 import "package:photos/core/event_bus.dart";
 import "package:photos/events/compute_control_event.dart";
+import "package:photos/main.dart";
 import "package:thermal/thermal.dart";
 
 enum _ComputeRunState {
@@ -42,6 +43,13 @@ class ComputeController {
 
   ComputeController() {
     _logger.info('ComputeController constructor');
+
+    // we don't need listeners to be initialized in background
+    if (isProcessBg) {
+      _logger.info('init done ');
+      return;
+    }
+
     _startInteractionTimer(kDefaultInteractionTimeout);
     if (Platform.isIOS) {
       if (kDebugMode) {
@@ -71,6 +79,8 @@ class ComputeController {
   }
 
   bool requestCompute({bool ml = false, bool stream = false}) {
+    // TODO: Remove after testing
+    return false;
     _logger.info("Requesting compute: ml: $ml, stream: $stream");
     if (!_isDeviceHealthy || !_canRunGivenUserInteraction()) {
       _logger.info("Device not healthy or user interacting, denying request.");
@@ -153,6 +163,7 @@ class ComputeController {
   }
 
   void _fireControlEvent() {
+    return;
     final shouldRunCompute = _isDeviceHealthy && _canRunGivenUserInteraction();
     if (shouldRunCompute != _canRunCompute) {
       _canRunCompute = shouldRunCompute;
@@ -175,6 +186,25 @@ class ComputeController {
     _startInteractionTimer(kDefaultInteractionTimeout);
   }
 
+  Future<bool> isDeviceHealthyFuture() async {
+    if (!isProcessBg) return isDeviceHealthy;
+
+    // Update Thermal status
+    _lastThermalStatus = await _thermal.thermalStatus;
+
+    // Update Battery info and device health
+    if (Platform.isIOS) {
+      _iosLastBatteryInfo = await BatteryInfoPlugin().iosBatteryInfo;
+      _isDeviceHealthy = _computeIsiOSDeviceHealthy();
+    } else {
+      _androidLastBatteryInfo = await BatteryInfoPlugin().androidBatteryInfo;
+      _isDeviceHealthy = _computeIsAndroidDeviceHealthy();
+    }
+
+    _logger.info("Device health status: $_isDeviceHealthy");
+    return _isDeviceHealthy;
+  }
+
   void _onAndroidBatteryStateUpdate(AndroidBatteryInfo? batteryInfo) {
     _androidLastBatteryInfo = batteryInfo;
     _logger.info("Battery info: ${batteryInfo!.toJson()}");

mobile/apps/photos/lib/services/machine_learning/face_ml/face_detection/face_detection_service.dart

@@ -54,12 +54,19 @@ class FaceDetectionService extends MlModel {
       'sessionAddress should be valid',
     );
 
+    _logger.info(
+      "Running face detection for image with size ${dimensions.width}x${dimensions.height}",
+    );
+
     final startTime = DateTime.now();
 
     final (inputImageList, scaledSize) = await preprocessImageYoloFace(
       dimensions,
       rawRgbaBytes,
     );
+    _logger.info(
+      "Preprocessed image to input list of size ${inputImageList.length} with scaled size $scaledSize",
+    );
     final preprocessingTime = DateTime.now();
     final preprocessingMs =
         preprocessingTime.difference(startTime).inMilliseconds;
@@ -68,8 +75,14 @@ class FaceDetectionService extends MlModel {
     List<List<List<double>>>? nestedResults = [];
     try {
       if (MlModel.usePlatformPlugin) {
+        _logger.info(
+          "Running inference using platform plugin",
+        );
         nestedResults = await _runPlatformPluginPredict(inputImageList);
       } else {
+        _logger.info(
+          "Running inference using ONNX runtime",
+        );
         nestedResults = _runFFIBasedPredict(
           sessionAddress,
           inputImageList,
@@ -116,9 +129,15 @@ class FaceDetectionService extends MlModel {
     final inputs = {'input': inputOrt};
     final runOptions = OrtRunOptions();
     final session = OrtSession.fromAddress(sessionAddress);
+    _logger.info(
+      "Running face detection using ONNX runtime with input size ${inputImageList.length}",
+    );
     final List<OrtValue?> outputs = session.run(runOptions, inputs);
     final result =
         outputs[0]?.value as List<List<List<double>>>; // [1, 25200, 16]
+    _logger.info(
+      "Finished running face detection using ONNX runtime",
+    );
     inputOrt.release();
     runOptions.release();
     for (var element in outputs) {
@@ -132,11 +151,18 @@ class FaceDetectionService extends MlModel {
     Float32List inputImageList,
   ) async {
     final OnnxDart plugin = OnnxDart();
+    _logger.info(
+      "Running face detection using OnnxDart plugin with input size ${inputImageList.length}",
+    );
     final result = await plugin.predict(
       inputImageList,
       _modelName,
     );
 
+    _logger.info(
+      "Finished running face detection using OnnxDart plugin",
+    );
+
     final int resultLength = result!.length;
     assert(resultLength % 25200 * 16 == 0);
     const int outerLength = 1;

mobile/apps/photos/lib/services/machine_learning/face_ml/face_recognition_service.dart

@@ -81,6 +81,9 @@ class FaceRecognitionService {
     final faceResults = <FaceResult>[];
     final startTime = DateTime.now();
 
+    _logger.info(
+      "Starting runFacesPipeline with fileID $enteFileID",
+    );
     // Get the faces
     final List<FaceDetectionRelative> faceDetectionResult =
         await _detectFacesSync(
@@ -90,6 +93,9 @@ class FaceRecognitionService {
       faceDetectionAddress,
       faceResults,
     );
+    _logger.info(
+      "Detected ${faceDetectionResult.length} faces in image with fileID $enteFileID",
+    );
     final detectFacesTime = DateTime.now();
     final detectFacesMs = detectFacesTime.difference(startTime).inMilliseconds;
 
@@ -101,6 +107,9 @@ class FaceRecognitionService {
       return [];
     }
 
+    _logger.info(
+      "Detected ${faceDetectionResult.length} faces, proceeding to alignment and embedding",
+    );
     // Align the faces
     final Float32List faceAlignmentResult = await _alignFacesSync(
       dim,
@@ -112,6 +121,9 @@ class FaceRecognitionService {
     final alignFacesMs =
         alignFacesTime.difference(detectFacesTime).inMilliseconds;
 
+    _logger.info(
+      "Aligned ${faceDetectionResult.length} faces in image with fileID $enteFileID",
+    );
     // Get the embeddings of the faces
     await _embedFacesSync(
       faceAlignmentResult,
@@ -139,6 +151,9 @@ class FaceRecognitionService {
     List<FaceResult> faceResults,
   ) async {
     try {
+      _logger.info(
+        "Running face detection for fileID $fileID with interpreter at $interpreterAddress",
+      );
       // Get the bounding boxes of the faces
       final List<FaceDetectionRelative> faces =
           await FaceDetectionService.predict(
@@ -147,6 +162,10 @@ class FaceRecognitionService {
         interpreterAddress,
       );
 
+      _logger.info(
+        "Detected ${faces.length} faces in image with fileID $fileID",
+      );
+
       // Add detected faces to the faceResults
       for (var i = 0; i < faces.length; i++) {
         faceResults.add(

mobile/apps/photos/lib/services/machine_learning/ml_service.dart

@@ -10,6 +10,7 @@ import "package:photos/db/files_db.dart";
 import "package:photos/db/ml/db.dart";
 import "package:photos/events/compute_control_event.dart";
 import "package:photos/events/people_changed_event.dart";
+import "package:photos/main.dart";
 import "package:photos/models/ml/face/face.dart";
 import "package:photos/models/ml/ml_versions.dart";
 import "package:photos/service_locator.dart";
@@ -69,31 +70,36 @@ class MLService {
     _logger.info("client: $client");
 
     // Listen on ComputeController
-    Bus.instance.on<ComputeControlEvent>().listen((event) {
-      if (!flagService.hasGrantedMLConsent) {
-        return;
-      }
+    /// Only listen for events when in foreground,
+    /// so we don't waste resources when the app is in background
+    /// and we just do things sequentially
+    if (!isProcessBg) {
+      Bus.instance.on<ComputeControlEvent>().listen((event) {
+        if (!flagService.hasGrantedMLConsent) {
+          return;
+        }
 
-      _mlControllerStatus = event.shouldRun;
-      if (_mlControllerStatus) {
-        if (_shouldPauseIndexingAndClustering) {
-          _cancelPauseIndexingAndClustering();
-          _logger.info(
-            "MLController allowed running ML, faces indexing undoing previous pause",
-          );
+        _mlControllerStatus = event.shouldRun;
+        if (_mlControllerStatus) {
+          if (_shouldPauseIndexingAndClustering) {
+            _cancelPauseIndexingAndClustering();
+            _logger.info(
+              "MLController allowed running ML, faces indexing undoing previous pause",
+            );
+          } else {
+            _logger.info(
+              "MLController allowed running ML, faces indexing starting",
+            );
+          }
+          unawaited(runAllML());
         } else {
           _logger.info(
-            "MLController allowed running ML, faces indexing starting",
+            "MLController stopped running ML, faces indexing will be paused (unless it's fetching embeddings)",
           );
+          pauseIndexingAndClustering();
         }
-        unawaited(runAllML());
-      } else {
-        _logger.info(
-          "MLController stopped running ML, faces indexing will be paused (unless it's fetching embeddings)",
-        );
-        pauseIndexingAndClustering();
-      }
-    });
+      });
+    }
 
     _isInitialized = true;
     _logger.info('init done');
@@ -136,7 +142,7 @@ class MLService {
         );
         await clusterAllImages();
       }
-      if (_mlControllerStatus == true) {
+      if (!isProcessBg && _mlControllerStatus == true) {
         // refresh discover section
         magicCacheService.updateCache(forced: force).ignore();
         // refresh memories section
@@ -148,7 +154,7 @@ class MLService {
       if ((await mlDataDB.getUnclusteredFaceCount()) > 0) {
         await clusterAllImages();
       }
-      if (_mlControllerStatus == true) {
+      if (!isProcessBg && _mlControllerStatus == true) {
         // refresh discover section
         magicCacheService.updateCache().ignore();
         // refresh memories section (only runs if forced is true)
@@ -160,8 +166,10 @@ class MLService {
     } finally {
       _logger.severe("ML finished running");
       _isRunningML = false;
-      computeController.releaseCompute(ml: true);
-      VideoPreviewService.instance.queueFiles();
+      if (!isProcessBg) {
+        computeController.releaseCompute(ml: true);
+        VideoPreviewService.instance.queueFiles();
+      }
     }
   }
 

mobile/apps/photos/lib/services/machine_learning/semantic_search/clip/clip_image_encoder.dart

@@ -86,6 +86,9 @@ class ClipImageEncoder extends MlModel {
     Float32List inputList,
   ) async {
     final OnnxDart plugin = OnnxDart();
+    _logger.info(
+      "Running Clip image predict using OnnxDart plugin with input size ${inputList.length}",
+    );
     final result = await plugin.predict(
       inputList,
       _modelName,

mobile/apps/photos/lib/services/machine_learning/semantic_search/semantic_search_service.dart

@@ -307,6 +307,9 @@ class SemanticSearchService {
     Uint8List rawRgbaBytes,
     int clipImageAddress,
   ) async {
+    _logger.info(
+      "Running Clip image encoding for file ID: $enteFileID",
+    );
     final embedding = await ClipImageEncoder.predict(
       dimensions,
       rawRgbaBytes,
@@ -314,6 +317,10 @@ class SemanticSearchService {
       enteFileID,
     );
 
+    _logger.info(
+      "Clip image encoding completed for file ID: $enteFileID",
+    );
+
     final clipResult = ClipResult(fileID: enteFileID, embedding: embedding);
 
     return clipResult;

mobile/apps/photos/lib/services/sync/remote_sync_service.dart

@@ -95,6 +95,10 @@ class RemoteSyncService {
   }
 
   Future<void> sync({bool silently = false}) async {
+    // TODO: remove
+    // if (!isProcessBg) {
+    //   return;
+    // }
     if (!_config.hasConfiguredAccount()) {
       _logger.info("Skipping remote sync since account is not configured");
       return;
@@ -377,10 +381,9 @@ class RemoteSyncService {
         localIDsToSync.removeAll(alreadyClaimedLocalIDs);
         if (alreadyClaimedLocalIDs.isNotEmpty && !_hasCleanupStaleEntry) {
           try {
-          await _db.removeQueuedLocalFiles(alreadyClaimedLocalIDs);
-          } catch(e, s) {
-            _logger.severe("removeQueuedLocalFiles failed",e,s);
-            
+            await _db.removeQueuedLocalFiles(alreadyClaimedLocalIDs);
+          } catch (e, s) {
+            _logger.severe("removeQueuedLocalFiles failed", e, s);
           }
         }
       }

mobile/apps/photos/lib/utils/bg_task_utils.dart

@@ -23,7 +23,8 @@ void callbackDispatcher() {
         try {
           BgTaskUtils.$.info('Task started $tlog');
           await runBackgroundTask(taskName, tlog).timeout(
-            Platform.isIOS ? kBGTaskTimeout : const Duration(hours: 1),
+            // TODO: For testing don't do seppuku
+            Platform.isIOS && false ? kBGTaskTimeout : const Duration(hours: 1),
             onTimeout: () async {
               BgTaskUtils.$.warning(
                 "TLE, committing seppuku for taskID: $taskName",

mobile/apps/photos/lib/utils/image_ml_util.dart

@@ -83,11 +83,17 @@ Future<DecodedImage> decodeImageFromPath(
         "Failed to decode image from file: $imagePath using image package",
       );
     }
-    final bytes = imageData.getBytes(order: img_pkg.ChannelOrder.rgba);
+
+    Uint8List? bytes;
+    for (final order in img_pkg.ChannelOrder.values) {
+      bytes = imageData.getBytes(order: order);
+      _logger.info("Bytes length is: ${bytes.length}, for order: : $order");
+    }
     final dimensions = Dimensions(
       width: image!.width,
       height: image.height,
     );
+    _logger.info("Dimensions are: $dimensions");
     return DecodedImage(
       dimensions: dimensions,
       rawRgbaBytes: bytes,
@@ -111,6 +117,9 @@ Future<DecodedImage> decodeImageFromPath(
   }
 
   late Image image;
+  _logger.info(
+    'Decoding image at path: $imagePath, format: $format, includeRgbaBytes: $includeRgbaBytes',
+  );
   try {
     image = await decodeImageFromData(imageData);
   } catch (e, s) {
@@ -144,12 +153,18 @@ Future<DecodedImage> decodeImageFromPath(
       );
     }
   }
+  _logger.info(
+    "Decoded image at path: $imagePath [i]",
+  );
   if (!includeRgbaBytes) {
     return DecodedImage(
       dimensions: Dimensions(width: image.width, height: image.height),
       image: includeDartUiImage ? image : null,
     );
   }
+  _logger.info(
+    "Getting Raw RGBA",
+  );
   final rawRgbaBytes = await _getRawRgbaBytes(image);
   return DecodedImage(
     dimensions: Dimensions(width: image.width, height: image.height),

mobile/apps/photos/lib/utils/isolate/super_isolate.dart

@@ -128,6 +128,8 @@ abstract class SuperIsolate {
       final taskID = newIsolateTaskID(operation.name);
       _mainSendPort.send([taskID, operation.index, args, answerPort.sendPort]);
 
+      logger.info("Activity ${operation.name} started");
+
       answerPort.listen((receivedMessage) {
         if (receivedMessage['taskID'] != taskID) {
           logger.severe("Received isolate message with wrong taskID");
@@ -136,6 +138,7 @@ abstract class SuperIsolate {
         final logs = receivedMessage['logs'] as List<String>;
         IsolateLogger.handLogStringsToMainLogger(logs);
         final data = receivedMessage['data'];
+
         if (data is Map && data.containsKey('error')) {
           // Handle the error
           final errorMessage = data['error'];
@@ -143,11 +146,13 @@ abstract class SuperIsolate {
           final exception = Exception(errorMessage);
           final stackTrace = StackTrace.fromString(errorStackTrace);
           completer.completeError(exception, stackTrace);
+          logger.severe("Activity ${operation.name} failed");
         } else {
           completer.complete(data);
+          logger.info("Activity ${operation.name} completed");
         }
+        _activeTasks--;
       });
-      _activeTasks--;
 
       return completer.future;
     });

mobile/apps/photos/lib/utils/ml_util.dart

@@ -7,6 +7,7 @@ import "package:photos/db/files_db.dart";
 import "package:photos/db/ml/db.dart";
 import "package:photos/db/ml/filedata.dart";
 import "package:photos/extensions/list.dart";
+import "package:photos/main.dart";
 import "package:photos/models/file/extensions/file_props.dart";
 import "package:photos/models/file/file.dart";
 import "package:photos/models/file/file_type.dart";
@@ -411,19 +412,28 @@ Future<MLResult> analyzeImageStatic(Map args) async {
     );
     final startTime = DateTime.now();
 
+    _logger.info("Decoding image at path: $imagePath");
     // Decode the image once to use for both face detection and alignment
     final decodedImage = await decodeImageFromPath(
       imagePath,
       includeRgbaBytes: true,
       includeDartUiImage: false,
+      inBackground: isProcessBg,
     );
     final rawRgbaBytes = decodedImage.rawRgbaBytes!;
     final imageDimensions = decodedImage.dimensions;
+    _logger.info(
+      "Decoded image with rgbaLength: ${rawRgbaBytes.length}, dimensions: $imageDimensions",
+    );
     final result = MLResult.fromEnteFileID(enteFileID);
     result.decodedImageSize = imageDimensions;
     final decodeTime = DateTime.now();
     final decodeMs = decodeTime.difference(startTime).inMilliseconds;
 
+    _logger.info(
+      "Decoded image at path: $imagePath, in $decodeMs ms",
+    );
+
     String faceMsString = "", clipMsString = "";
     final pipelines = await Future.wait([
       runFaces